Publications of Geert Litjens

Accepted or to appear

Papers in international journals

  1. H. Pinckaers, B. van Ginneken and G. Litjens. "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", IEEE Transactions on Pattern Analysis and Machine Intelligence. Abstract/PDF DOI PMID


2020

Papers in international journals

  1. W. Bulten, M. Balkenhol, J.-J.A. Belinga, A. Brilhante, A. Çakır, L. Egevad, M. Eklund, X. Farre, K. Geronatsiou, V. Molinie, G. Pereira, P. Roy, G. Saile, P. Salles, E. Schaafsma, J. Tschui, A.-M. Vos, B. Delahunt, H. Samaratunga, D.J. Grignon, A.J. Evans, D.M. Berney, C.-C. Pan, G. Kristiansen, J.G. Kench, J. Oxley, K.R.M. Leite, J.K. McKenney, P.A. Humphrey, S.W. Fine, T. Tsuzuki, M. Varma, M. Zhou, E. Comperat, D.G. Bostwick, K.A. Iczkowski, C. Magi-Galluzzi, J.R. Srigley, H. Takahashi, T. van der Kwast, H. van Boven, R. Vink, J. van der Laak, C. Hulsbergen-van der Kaa and G. Litjens. "Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists", Modern Pathology 2020. Abstract/PDF DOI PMID

  2. W. Bulten, H. Pinckaers, H. van Boven, R. Vink, T. de Bel, B. van Ginneken, J. van der Laak, C. Hulsbergen-van de Kaa and G. Litjens. "Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study", Lancet Oncology 2020;21:233-241. Abstract/PDF DOI arXiv PMID


2019

Papers in international journals

  1. L. Aprupe, G. Litjens, T.J. Brinker, J. van der Laak and N. Grabe. "Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks", PeerJ 2019;7:e6335. Abstract/PDF DOI PMID

  2. P. Bandi, M. Balkenhol, B. van Ginneken, J. van der Laak and G. Litjens. "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks", PeerJ 2019;7:e8242. Abstract/PDF DOI PMID

  3. W. Bulten, P. Bandi, J. Hoven, R. van de Loo, J. Lotz, N. Weiss, J. van der Laak, B. van Ginneken, C. Hulsbergen-van de Kaa and G. Litjens. "Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard", Nature Scientific Reports 2019;9(864). Abstract/PDF DOI arXiv PMID

  4. O.A. Debats, G.J.S. Litjens and H.J. Huisman. "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks", PeerJ 2019;7:e8052. Abstract/PDF DOI PMID

  5. O. Geessink, A. Baidoshvili, J. Klaase, B. Ehteshami Bejnordi, G. Litjens, G. van Pelt, W. Mesker, I. Nagtegaal, F. Ciompi and J. van der Laak. "Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer", Cellular Oncology 2019:1-11. Abstract/PDF DOI PMID

  6. J. van der Laak, F. Ciompi and G. Litjens. "No pixel-level annotations needed", Nature Biomedical Engineering 2019;3:855-856. PDF DOI PMID

  7. G. Litjens, F. Ciompi, J.M. Wolterink, B.D. de Vos, T. Leiner, J. Teuwen and I. Isgum. "State-of-the-Art Deep Learning in Cardiovascular Image Analysis", JACC Cardiovascular Imaging 2019;12:1549-1565. Abstract/PDF DOI PMID

  8. M.C. Maas, G.J.S. Litjens, A.J. Wright, U.I. Attenberger, M.A. Haider, T.H. Helbich, B. Kiefer, K.J. Macura, D.J.A. Margolis, A.R. Padhani, K.M. Selnaes, G.M. Villeirs, J.J. Futterer and T.W.J. Scheenen. "A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach", Investigative Radiology 2019. Abstract/PDF DOI PMID

  9. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, M. Sherman, A. Polonia, J. Parry, M. Abubakar, G. Litjens, J. van der Laak and F. Ciompi. "Learning to detect lymphocytes in immunohistochemistry with deep learning", Medical Image Analysis 2019;58:101547. Abstract/PDF DOI PMID

  10. D. Tellez, G. Litjens, P. Bandi, W. Bulten, J.-M. Bokhorst, F. Ciompi and J. van der Laak. "Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology", Medical Image Analysis 2019;58:101544. Abstract/PDF DOI PMID

  11. D. Tellez, G. Litjens, J. van der Laak and F. Ciompi. "Neural Image Compression for Gigapixel Histopathology Image Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence 2019;58:101544. Abstract/PDF DOI PMID


Preprints

  1. H. Pinckaers, B. van Ginneken and G. Litjens. "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", arXiv:1911.04432 2019. Abstract

  2. A.L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B.A. Landman, G. Litjens, B. Menze, O. Ronneberger, R.M. Summers, P. Bilic, P.F. Christ, R.K.G. Do, M. Gollub, J. Golia-Pernicka, S.H. Heckers, W.R. Jarnagin, M.K. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein and M.J. Cardoso. "A large annotated medical image dataset for the development and evaluation of segmentation algorithms", arXiv:1902.09063 2019. Abstract arXiv


Papers in conference proceedings

  1. T. de Bel, M. Hermsen, J. Kers, J. van der Laak and G. Litjens. "Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology", in: Medical Imaging with Deep Learning, 2019. Abstract/PDF URL

  2. K. Dercksen, W. Bulten and G. Litjens. "Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification", in: Medical Imaging with Deep Learning, 2019. Abstract/PDF URL

  3. H. Pinckaers, W. Bulten and G. Litjens. "High resolution whole prostate biopsy classification using streaming stochastic gradient descent", in: Medical Imaging of Proceedings of the SPIE, 2019, page 1. Abstract/PDF DOI


Abstracts

  1. W. Bulten, H. Pinckaers, C. Hulsbergen-van de Kaa and G. Litjens. "Automated Gleason Grading of Prostate Biopsies Using Deep Learning", in: United States and Canadian Academy of Pathology (USCAP) 108th Annual Meeting, 2019. Abstract


2018

Papers in international journals

  1. P. Bandi, O. Geessink, Q. Manson, M. van Dijk, M. Balkenhol, M. Hermsen, B.E. Bejnordi, B. Lee, K. Paeng, A. Zhong, Q. Li, F.G. Zanjani, S. Zinger, K. Fukuta, D. Komura, V. Ovtcharov, S. Cheng, S. Zeng, J. Thagaard, A.B. Dahl, H. Lin, H. Chen, L. Jacobsson, M. Hedlund, M. Cetin, E. Halici, H. Jackson, R. Chen, F. Both, J. Franke, H. Kusters-Vandevelde, W. Vreuls, P. Bult, B. van Ginneken, J. van der Laak and G. Litjens. "From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge", IEEE Transactions on Medical Imaging 2018;38:550-560. Abstract/PDF DOI PMID

  2. B.E. Bejnordi, G. Litjens and J.A. van der Laak. "Machine Learning Compared With Pathologist Assessment-Reply", Journal of the American Medical Association 2018;319:1726. PDF DOI PMID

  3. G. Litjens, P. Bandi, B. Ehteshami Bejnordi, O. Geessink, M. Balkenhol, P. Bult, A. Halilovic, M. Hermsen, R. van de Loo, R. Vogels, Q. Manson, N. Stathonikos, A. Baidoshvili, P. van Diest, C. Wauters, M. van Dijk and J. van der Laak. "1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset", GigaScience 2018;7(6):1-8. Abstract/PDF DOI PMID

  4. D. Tellez, M. Balkenhol, I. Otte-Holler, R. van de Loo, R. Vogels, P. Bult, C. Wauters, W. Vreuls, S. Mol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi. "Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks", IEEE Transactions on Medical Imaging 2018;37(9):2126 - 2136. Abstract/PDF DOI PMID


Papers in conference proceedings

  1. W. Bulten, C.A.H. van de Kaa, J. van der Laak and G.J.S. Litjens. "Automated segmentation of epithelial tissue in prostatectomy slides using deep learning", in: Medical Imaging, volume 10581 of Proceedings of the SPIE, 2018, page 105810S. Abstract/PDF DOI

  2. W. Bulten and G. Litjens. "Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders", in: Medical Imaging with Deep Learning, 2018. Abstract/PDF URL

  3. D. Geijs, M. Intezar, J. van der Laak and G. Litjens. "Automatic color unmixing of IHC stained whole slide images", in: Medical Imaging, volume 10581 of Proceedings of the SPIE, 2018. Abstract/PDF DOI

  4. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, G. Litjens, J. van der Laak and F. Ciompi. "Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images", in: Medical Imaging with Deep Learning, 2018. Abstract/PDF URL

  5. D. Tellez, M. Balkenhol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi. "H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection", in: Medical Imaging, volume 10581 of Proceedings of the SPIE, 2018. Abstract/PDF DOI


2017

Papers in international journals

  1. B.E. Bejnordi, G. Zuidhof, M. Balkenhol, M. Hermsen, P. Bult, B. van Ginneken, N. Karssemeijer, G. Litjens and J. van der Laak. "Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images", Journal of Medical Imaging 2017;4:044504. Abstract/PDF DOI PMID

  2. M.U. Dalmis, G. Litjens, K. Holland, A. Setio, R. Mann, N. Karssemeijer and A. Gubern-Merida. "Using deep learning to segment breast and fibroglandular tissue in MRI volumes", Medical Physics 2017;44:533-546. Abstract/PDF DOI PMID

  3. B. Ehteshami Bejnordi, M. Veta, P.J. van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J.A.W.M. van der Laak, the CAMELYON16 Consortium, M. Hermsen, Q.F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M.C. van Dijk, P. Bult, F. Beca, A.H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M.M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu and R. Venâncio. "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer", Journal of the American Medical Association 2017;318:2199-2210. Abstract/PDF DOI PMID

  4. M. Ghafoorian, N. Karssemeijer, T. Heskes, I. van Uden, C.I. Sanchez, G. Litjens, F.-E. de Leeuw, B. van Ginneken, E. Marchiori and B. Platel. "Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities", Nature Scientific Reports 2017;7(1):5110. Abstract/PDF DOI arXiv PMID

  5. T. Kooi, G. Litjens, B. van Ginneken, A. Gubern-Merida, C.I. Sanchez, R. Mann, A. den Heeten and N. Karssemeijer. "Large scale deep learning for computer aided detection of mammographic lesions", Medical Image Analysis 2017;35:303-312. Abstract/PDF DOI PMID

  6. S. Laban, G. Giebel, N. Klümper, A. Schröck, J. Doescher, G. Spagnoli, J. Thierauf, M. Theodoraki, R. Remark, S. Gnjatic, R. Krupar, A. Sikora, G. Litjens, N. Grabe, G. Kristiansen, F. Bootz, P. Schuler, C. Brunner, J. Brägelmann, T. Hoffmann and S. Perner. "MAGE expression in head and neck squamous cell carcinoma primary tumors, lymph node metastases and respective recurrences: implications for immunotherapy", Oncotarget 2017;8:14719-14735. Abstract/PDF DOI PMID

  7. G. Litjens, T. Kooi, B. Ehteshami Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken and C.I. Sanchez. "A Survey on Deep Learning in Medical Image Analysis", Medical Image Analysis 2017;42:60-88. Abstract/PDF DOI arXiv PMID


Papers in conference proceedings

  1. P. Bandi, R. van de Loo, M. Intezar, D. Geijs, F. Ciompi, B. van Ginneken, J. van der Laak and G. Litjens. "Comparison of Different Methods for Tissue Segmentation In Histopathological Whole-Slide Images", in: IEEE International Symposium on Biomedical Imaging, 2017, pages 591-595. Abstract/PDF DOI arXiv

  2. F. Ciompi, O.G.F. Geessink, B.E. Bejnordi, G.S. de Souza, A. Baidoshvili, G. Litjens, B. van Ginneken, I.D. Nagtegaal and J.A. van der Laak. "The importance of stain normalization in colorectal tissue classification with convolutional networks", in: IEEE International Symposium on Biomedical Imaging, 2017, pages 160-163. Abstract/PDF DOI arXiv


Abstracts

  1. M. Hermsen, T. de Bel, M. van de Warenburg, J. Knuiman, E. Steenbergen, G. Litjens, B. Smeets, L. Hilbrands and J. van der Laak. "Automatic segmentation of histopathological slides from renal allograft biopsies using artificial intelligence", in: Dutch Federation of Nephrology (NfN) Fall Symposium, 2017. Abstract/PDF


2016

Papers in international journals

  1. B.E. Bejnordi, M. Balkenhol, G. Litjens, R. Holland, P. Bult, N. Karssemeijer and J. van der Laak. "Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images", IEEE Transactions on Medical Imaging 2016;35(9):2141-2150. Abstract/PDF DOI PMID

  2. B.E. Bejnordi, G. Litjens, N. Timofeeva, I. Otte-Holler, A. Homeyer, N. Karssemeijer and J. van der Laak. "Stain specific standardization of whole-slide histopathological images", IEEE Transactions on Medical Imaging 2016;35(2):404-415. Abstract/PDF DOI PMID

  3. O.A. Debats, A.S. Fortuin, H.J.M. Meijer, T. Hambrock, G.J.S. Litjens, J.O. Barentsz and H.J. Huisman. "Intranodal signal suppression in pelvic MR lymphography of prostate cancer patients: a quantitative comparison of ferumoxtran-10 and ferumoxytol", PeerJ 2016;4:e2471. Abstract/PDF DOI PMID

  4. O.A. Debats, M. Meijs, G.J.S. Litjens and H.J. Huisman. "Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients", Medical Physics 2016;43(6):3132. Abstract/PDF DOI PMID

  5. G. Litjens, C.I. Sanchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken and J. van der Laak. "Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis", Nature Scientific Reports 2016;6:26286. Abstract/PDF DOI PMID

  6. G.J.S. Litjens, R. Elliott, N.N.C. Shih, M.D. Feldman, T. Kobus, C. Hulsbergen-van de Kaa, J.O. Barentsz, H.J. Huisman and A. Madabhushi. "Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.", Radiology 2016;278(1):135-145. Abstract DOI PMID

  7. R. Remark, T. Merghoub, N. Grabe, G. Litjens, D. Damotte, J.D. Wolchok, M. Merad and S. Gnjatic. "In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide", Science Immunology 2016;1(1):aaf6925-aaf6925. Abstract/PDF DOI PMID

  8. A.A.A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. van Riel, M.W. Wille, M. Naqibullah, C.I. Sanchez and B. van Ginneken. "Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks", IEEE Transactions on Medical Imaging 2016;35(5):1160-1169. Abstract/PDF DOI PMID


Papers in conference proceedings

  1. G. Litjens, K. Safferling and N. Grabe. "Automated robust registration of grossly misregistered whole-slide images with varying stains", in: Medical Imaging, volume 9791 of Proceedings of the SPIE, 2016, page 979103. Abstract/PDF DOI


2015

Papers in international journals

  1. G.J.S. Litjens, J.O. Barentsz, N. Karssemeijer and H.J. Huisman. "Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI", European Radiology 2015;25(11):3187-3199. Abstract/PDF DOI PMID


Papers in conference proceedings

  1. B.E. Bejnordi, G. Litjens, M. Hermsen, N. Karssemeijer and J.A.W.M. van der Laak. "A multi-scale superpixel classification approach for region of interest detection in whole slide histopathology images", in: Medical Imaging, volume 9420 of Proceedings of the SPIE, 2015, page 94200H. Abstract/PDF DOI

  2. G. Litjens, B.E. Bejnordi, N. Timofeeva, G. Swadi, I. Kovacs, C.A. Hulsbergen-van de Kaa and J.A.W.M. van der Laak. "Automated detection of prostate cancer in digitized whole-slide images of H&E-stained biopsy specimens", in: Medical Imaging, volume 9420 of Proceedings of the SPIE, 2015, page 94200B. Abstract/PDF DOI


Theses

  1. G.J.S. Litjens. "Computerized detection of cancer in multi-parametric prostate MRI", PhD thesis, Radboud University, Nijmegen, 2015. Abstract/PDF URL


2014

Papers in international journals

  1. G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33(5):1083-1092. Abstract/PDF DOI PMID

  2. G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P.E. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman and A. Madabhushi. "Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge", Medical Image Analysis 2014;18(2):359-373. Abstract/PDF DOI PMID

  3. G.J. Litjens, H.J. Huisman, R.M. Elliott, N.N. Shih, M.D. Feldman, S. Viswanath, J.J. Fütterer, J.G. Bomers and A. Madabhushi. "Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy", Journal of Medical Imaging 2014;1(3):035001-035001. PDF DOI PMID


Papers in conference proceedings

  1. G. Litjens, R. Elliott, N. Shih, M. Feldman, J. Barentsz, C. Hulsbergen van de Kaa, I. Kovacs, H. Huisman and A. Madabhushi. "Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI", in: Medical Imaging, volume 9035 of Proceedings of the SPIE, 2014, page 903512. Abstract/PDF DOI

  2. G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, Fütterer, J. Bomers and A. Madabhushi. "Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation", in: Medical Imaging, volume 9036 of Proceedings of the SPIE, 2014, page 90361D. Abstract/PDF DOI


Abstracts

  1. G. Litjens, N. Karssemeijer, J.O. Barentsz and H. Huisman. "Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging", in: Annual Meeting of the Radiological Society of North America, 2014. Abstract

  2. E. Vos, T. Kobus, G. Litjens, T. Hambrock, C.H. van de Kaa, M. Maas and T. Scheenen. "Multiparametric MR imaging for the assessment of prostate cancer aggressiveness at 3 Tesla", in: Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2014.


2013

Papers in international journals

  1. K.N.A. Nagel, M.G. Schouten, T. Hambrock, G. Litjens, C.M.A. Hoeks, B.T. Haken, J.O. Barentsz and J.J. Fütterer. "Differentiation of Prostatitis and Prostate Cancer by Using Diffusion-weighted MR Imaging and MR-guided Biopsy at 3 T", Radiology 2013;267:164-172. Abstract/PDF DOI PMID

  2. E.K. Vos, G. Litjens, T. Kobus, T. Hambrock, C.A. Kaa, J.O. Barentsz, H. Huisman and T.W. Scheenen. "Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3T", European Urology 2013;64:448-455. PDF URL PMID


Abstracts

  1. G. Litjens, J.O. Barentsz, N. Karssemeijer and H. Huisman. "Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?", in: Annual Meeting of the Radiological Society of North America, 2013. Abstract

  2. M.C. Maas, M.J. Koopman, G.J. Litjens, A.J. Wright, K.M. Selnas, I.S. Gribbestad, M.A. Haider, K.J. Macura, D.J. Margolis, B. Kiefer, J.J. Fütterer and T.W. Scheenen. "Prostate Cancer localization with a Multiparametric MR Approach (PCaMAP): initial results of a multi-center study", in: Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2013. PDF


2012

Papers in international journals

  1. G. Litjens, T. Hambrock, C. Hulsbergen-van de Kaa, J. Barentsz and H. Huisman. "Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness", Radiology 2012;265(1):260-266. Abstract/PDF DOI PMID


Papers in conference proceedings

  1. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman. "Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach", in: Medical Imaging, volume 8315 of Proceedings of the SPIE, 2012, page 1, pages 83150G-83150G-6. Abstract/PDF DOI

  2. G. Litjens, O.A. Debats, W.J.M. van de Ven, N. Karssemeijer and H.J. Huisman. "A pattern recognition approach to zonal segmentation of the prostate on MRI", in: Medical Image Computing and Computer-Assisted Intervention, volume 7511 of Lecture Notes in Computer Science, 2012, pages 413-420. Abstract/PDF DOI

  3. G. Litjens, N. Karssemeijer and H.J. Huisman. "A multi-atlas approach for prostate segmentation in MRI", in: MICCAI Workshop: Prostate Cancer Imaging: The PROMISE12 Prostate Segmentation Challenge, 2012. PDF


Abstracts

  1. G. Litjens, J.O. Barentsz, N. Karssemeijer and H. Huisman. "Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI", in: Annual Meeting of the Radiological Society of North America, 2012. Abstract

  2. E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. Hulsbergen van de Kaa, H. Huisman and T. Scheenen. "Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T", in: Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2012. PDF


2011

Papers in international journals

  1. O.A. Debats, G. Litjens, J.O. Barentsz, N. Karssemeijer and H. Huisman. "Automated 3-Dimensional Segmentation of Pelvic Lymph Nodes in Magnetic Resonance Images", Medical Physics 2011;38(11):6178-6187. Abstract/PDF DOI PMID


Papers in conference proceedings

  1. G. Litjens, P. Vos, J. Barentsz, N. Karssemeijer and H. Huisman. "Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI", in: Medical Imaging, volume 7963 of Proceedings of the SPIE, 2011, page 1. Abstract/PDF DOI

  2. W.J.M. van de Ven, G. Litjens, J.O. Barentsz, T. Hambrock and H.J. Huisman. "Required accuracy of MR-US registration for prostate biopsies", in: Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions, volume 6963 of Lecture Notes in Computer Science, 2011, pages 92-99. Abstract/PDF DOI


Abstracts

  1. O. Debats, T. Hambrock, G. Litjens, H. Huisman and J. Barentsz. "Detection of Lymph Node Metastases with Ferumoxtran-10 vs Ferumoxytol", in: Annual Meeting of the Radiological Society of North America, 2011. Abstract

  2. G. Litjens, J.O. Barentsz, N. Karssemeijer and H. Huisman. "Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI", in: Annual Meeting of the Radiological Society of North America, 2011. Abstract

  3. M.G. Schouten, K. Nagel, T. Hambrock, C. Hoeks, G. Litjens, J. Barentsz and J. Fütterer. "Differentiation of Normal Prostate Tissue, Prostatitis, and Prostate Cancer: Correlation between Diffusion-weighted Imaging and MR-guided Biopsy", in: Annual Meeting of the Radiological Society of North America, 2011. Abstract


2010

Papers in conference proceedings

  1. H. Huisman, P. Vos, G. Litjens, T. Hambrock and J. Barentsz. "Computer aided detection of prostate cancer using t2w, DWI and DCE-MRI: methods and clinical applications", in: MICCAI Workshop: Prostate Cancer Imaging: Computer Aided Diagnosis, Prognosis, and Intervention, 2010. Abstract/PDF

  2. G. Litjens, M. Heisen, J. Buurman and B. ter Haar Romeny. "Pharmacokinetic models in clinical practice: what model to use for DCE-MRI of the breast?", in: IEEE International Symposium on Biomedical Imaging, 2010, pages 185-188. Abstract/PDF

  3. G. Litjens, L. Hogeweg, A. Schilham, P. de Jong, M. Viergever and B. van Ginneken. "Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates", in: Medical Image Computing and Computer-Assisted Intervention, volume 6362 of Lecture Notes in Computer Science, 2010, pages 396-403. Abstract/PDF DOI PMID

  4. P.R. Snoeren, G. Litjens, B. van Ginneken and N. Karssemeijer. "Training a Computer Aided Detection System with Simulated Lung Nodules in Chest Radiographs", in: The Third International Workshop on Pulmonary Image Analysis, 2010, pages 139-149. Abstract/PDF


2009

Abstracts

  1. G. Litjens, M. Heisen, J. Buurman, A. Wood, M. Medved, G. Karczmar and B.T. Haar-Romeny. "T1 Quantification: Variable Flip Angle Method vs Use of Reference Phantom", in: Annual Meeting of the Radiological Society of North America, 2009. Abstract


Theses

  1. G. Litjens. "Pharmacokinetic modeling in breast cancer MRI", Masters thesis, Eindhoven University of Technology, 2009. Abstract/PDF